BenevolentAI, founded in 2013, creates and applies AI technologies to transform the way medicines are discovered and developed. BenevolentAI seeks to improve patient's lives by applying technology designed to generate better data decision making and in doing so lower drug development costs, decrease failure rates and increase the speed at which medicines are generated. The company has developed the Benevolent Platform - a discovery platform used by BenevolentAI scientists to find new ways to treat disease and personalise drugs to patients. BenevolentAI is HQ'd in London with a research facility in Cambridge (UK) and further offices in New York and Antwerp. BenevolentAI has active R&D drug programmes from discovery to PhaseIIb in disease areas such as ALS, Parkinson's, Ulcerative Colitis and Sarcopenia.
Precision medicine first emerged in the late '90s. But it was the completion of the Human Genome Project in 2003 that really opened up a world of opportunity in this area because it became possible to test patients for certain biomarkers that could help match patients to more tailored and appropriate treatments. That approach is really at the heart of precision medicine, which is all about using genetic information to treat the right patient at the right time with the right therapy. This individualistic approach is exciting for patients who feel that rather than relying on a one-size-fits-all treatment, the therapies developed through precision medicine are more likely to be effective for their particular needs. And there's no doubt that drug makers are eager to explore the possibilities that come with the increased availability of genetic data and analytical tools.
Personalized cancer medicine has advanced from a distant hope to a clinical reality. Oncologists regularly individualize treatments to target a tumor's unique genetic weaknesses. But because these personalized medicines reach healthy tissues and tumors alike, even the most targeted treatments can cause unwanted side-effects. A new approach devised by nanotechnology experts at the Sloan Kettering Institute (SKI) at Memorial Sloan Kettering Cancer Center may improve the precision of personalized medicines by helping them avoid collateral damage. "We found a way to use machine-learning algorithms to design powerful nanomedicines that can deliver a stronger, safer, more personalized punch," says Daniel Heller, PhD, a chemist in the molecular pharmacology program at SKI and an assistant professor at the Weill Cornell Graduate School of Medical Sciences.
The spread of wearable digital technologies in healthcare generating big data entailed the appearance of a new type of medical information. They produce actionable insights into the biological state of individuals, just as "general" biomarkers, but are collected through digital tools. Here's my summary of what digital biomarkers mean and how they will be used in the near future. In the last couple of years, Fitbit, Misfit, Jawbone, Apple Health, Sleep as Android, WIWE, MocaCare, Skeeper – in other words, fitness trackers, step counters, health apps, sleep sensors, pocket ECG, blood pressure or other health parameter measuring devices appeared out of nowhere. By now, they constitute significant players on the health, wellness and fitness market; generating an astounding amount of data about patients and individuals not getting patient care.